Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: The first silicon for Wi-Fi 7, Google’s search quality “is” failing, and what Severance tells us about The Great Resignation. (more…)
Microsoft has teamed up with vFunction to make it easier to convert monolithic Java applications into a set of microservices that can be deployed on the Microsoft Azure cloud.
The Microsoft Services Industry Solutions group is making available the Java Refactoring Service, through which IT teams will be able to both lift and shift Java applications to the cloud and then convert them into a set of microservices using agent software and the vFunction Application Transformation Engine, which is infused with artificial intelligence (AI), to analyze Java code.
vFunction CEO Moti Rafalin said the goal is to eliminate the heavy lifting currently required to shift Java applications into the cloud. The vFunction Application Transformation Engine is required because most of the Java applications that were easy to move to the cloud have been, Rafalin noted. The remaining Java applications, he said, have levels of dependencies that make them too complex to move to the cloud without the aid of a static byte code analysis platform capable of parsing more than 10 million lines of code and thousands of Java class libraries.
Once those applications are converted into a set of microservices, it then becomes possible to achieve a level of scale that would otherwise be beyond the reach of any on-premises IT environment, he added, noting the same vFunction platform can be used also to help organizations continuously optimize microservices applications as they continue to evolve in a cloud computing environment.
Longer-term, vFunction is also looking to add support for Microsoft.Net applications that need to be migrated to the cloud, said Rafalin. Many complex Java and Microsoft.Net applications are still running in an on-premises IT environment because organizations are encountering inertia that arises from paralysis of analysis, he noted.
In general, Microsoft continues to gain momentum in its battle for cloud supremacy against Amazon Web Services (AWS). It’s not clear at what rate Java applications are being shifted to the cloud. However, most monolithic applications have components that would be easier to maintain and support as a distinct microservice. In general, organizations are embracing microservices to build applications that are more resilient. In the event a microservice becomes unavailable, requests for services are rerouted to another microservice to make sure the application degrades gracefully, rather than simply crashing altogether. The challenge, of course, is that over time the dependencies that can exist between hundreds of microservices can be more challenging to manage than those of the monolithic application being replaced.
It’s not likely that microservices-based applications with entire replace monolithic applications anytime soon, but the bulk of new applications are being built on top of a microservices-based framework. As such, many DevOps teams are now managing a broad mix of monolithic and microservices-based applications across an increasingly complex IT environment. Less clear is to what degree teams will be able to leverage existing DevOps tools and platforms to build and deploy both types of applications at the scale enterprise IT organizations require.
Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: More than 100,000 tech workers leave Russia, Perforce buys Puppet, and Intel moves faster than planned. (more…)
A global survey of 1,500 IT decision-makers conducted by F5 found that, on average, organizations are managing more than 200 applications deployed across an extended enterprise as well as more than 20 different security and delivery technologies.
Lori MacVittie, principal technical evangelist in the Office of the CTO for F5, said that level of complexity makes it apparent that IT teams will need to embrace higher levels of automation to manage the IT environments that are now at the core of a wide range of digital business transformation initiatives. The survey revealed that more than three-quarters of respondents (77%) were embracing site reliability engineering (SRE) principles to automate the management of IT.
A full 95% of respondents also planned to mine operational data for insights they could use to improve the customer experience and drive business growth. However, 98% of respondents also said they are currently unable to extract those needed insights from their existing systems. A total of 90% said they planned to implement some form of artificial intelligence (AI) to achieve that goal. More than two-thirds of respondents said their organizations realized that creating superior digital interactions for customers will require modernizing business processes and back-office functions.
As part of those efforts, the survey suggested that application workloads are being shifted across highly distributed computing environments. In addition to the 84% of respondents that are planning to move workloads to the edge, the survey found two-thirds of organizations (67%) are currently or planning to repatriate applications from the cloud to a data center environment in the next 12 months. A total of 93% of respondents reported using some type of as-a-service offering delivered via the cloud today.
Naturally, securing that many platforms also represents a major challenge. However, despite inherent risks, more than three-quarters (76%) of respondents said, given the choice, they’d turn off security measures to improve performance. That lack of willingness to sacrifice performance suggests that attitudes to security may not have changed much despite all the breaches that now regularly occur, said MacVittie.
It’s not clear to what degree managing enterprise IT environments has become untenable; however, it’s clear that legacy manual processes will need to become automated. Organizations can’t afford to keep hiring IT staff to manage IT platforms that span everything from the network edge to public clouds. In fact, the survey makes clear that DevOps practices—in the form of the SRE principles first defined by Google—are gaining traction. That doesn’t necessarily mean everyone managing IT is becoming an SRE or an engineer, but it does suggest that automation frameworks are being more widely used.
It may be a while before those automation frameworks are ubiquitous. However, one way or another, many of the tasks that have historically conspired to make working in IT tedious are going to disappear. The issue, of course, is to not wind up with islands of disconnected automation that introduce more complexity.
For those who doubted the digital transformation trend, it’s become clear that it’s not a marketing fad or a buzzword but is now standard operating protocol for businesses. A forecast released this week from market research firm Gartner confirms this, as the projected increase in application software, infrastructure software and managed services spending highlights that digital transformation is a CIO marathon and not a sprint.
According to Gartner, every major consumer-focused online offering and mobile application is underpinned by infrastructure-as-a-service and comprises a substantial portion of the near 10% growth in software spending that will occur this year.
Gartner said it expects digital business initiatives, such as experiential customer experience and optimization of supply chains, to push spending on enterprise applications and infrastructure software into double-digit growth next year.
Gartner’s prediction is that worldwide IT spending is projected to total $4.4 trillion in 2022, an increase of 4% from 2021.
But the technology battleground isn’t just about hardware and software, it’s also about talent. The tight IT talent pool is driving up salaries and forcing technology service providers to increase their prices. This is pushing up spending growth in analytics, cloud computing, seamless customer experiences and security
Gartner expects software spending to grow 9.8% to $674.9 billion in 2022; IT services are forecast to grow 6.8% to reach $1.3 trillion.
Cloud’s gain is traditional IT’s loss, according to a Gartner market forecast released earlier this year. Enterprise spending on public cloud computing in addressable segments will be greater than spending on traditional IT by 2025. Gartner’s ‘cloud shift’ research includes only those enterprise IT categories that can transition to cloud, within the application software, infrastructure software, business process services and system infrastructure markets. “By 2025, 51% of IT spending in these four categories will have shifted from traditional solutions to the public cloud, compared to 41% in 2022. Almost two-thirds (65.9%) of spending on application software will be directed toward cloud technologies in 2025, up from 57.7% in 2022,” Gartner predicted.
In 2022, traditional offerings will constitute 58.7% of the addressable revenue, but growth in traditional markets will be much lower than in the cloud. Demand for integration capabilities, agile work processes and composable architecture will drive a continued shift to the cloud, as long-term digital transformation and modernization initiatives are brought forward to 2022. Technology product managers should use the cloud shift as a measure of market opportunity.
DevSecOps is everywhere, and chances are your organization is shifting toward this convergence of development, security and operations. In an on-premises environment, you build your own DevSecOps process by mixing and matching existing and new tools. However, in the cloud, much of the environment is determined by your cloud provider. Microsoft’s Azure cloud is known for its robust security capabilities. Specifically, Microsoft has developed a set of capabilities and services that can help you ease into a DevSecOps workflow without much effort. The tools are pre-integrated and support everything from CI/CD to identity management. Let’s see how they work.
Security is an important part of DevOps. But it can be challenging to ensure that software developed in a CI/CD process is really secure.
DevSecOps is an organizational pattern that can make this possible, even at high development velocity. It is an approach the DevOps team can use to prevent security issues from the outset of the development life cycle, ensuring their software is not only high quality but also secure and resilient to failure.
DevSecOps is a discipline that combines application development, security, operations and infrastructure-as-code (IaC) with an automated continuous delivery cycle. Here are three practices that can help a team move towards a DevSecOps process:
The Azure cloud provides several built-in capabilities that make it possible to adopt a DevSecOps process.
Azure DevOps provides developer services that enable teams to plan tasks, collaborate on code development and build and deploy applications. Azure DevOps supports a collaborative culture and process in which developers, project managers and contributors work together to develop software. You can use Azure DevOps Services in the cloud or Azure DevOps Server on-premises.
You can integrate Azure Pipelines, the Azure cloud’s CI/CD solution, with your Kubernetes clusters. Use the same YAML document to build multi-stage CI/CD pipelines.
Azure Pipelines lets you track metadata, such as commit hashes and issue numbers, from Azure Boards into container images. This provides direct traceability from any security issue to a specific change made in development. It also provides clear, easy-to-read documentation that can improve the feedback loop between development, operations and security teams.
When developing Kubernetes applications, there is a need to test applications locally and understand how they interact with dependent services. You may need to develop and test multiple services in collaboration with other developers or teams.
Azure provides Bridge to Kubernetes, which lets you run and debug code on your development machine while connecting to your Kubernetes cluster. You can test your code end-to-end, set breakpoints on code running on the cluster and share your development cluster among team members. This makes it possible to test and resolve Kubernetes security issues in a realistic environment before deploying to production.
The Microsoft Identity Platform takes the Azure Active Directory (Azure AD) developer platform one step further. It allows applications to accept logins from any Microsoft identity, and obtain tokens that can be used to call Microsoft APIs or APIs created by other developers. This creates a large, interoperable ecosystem.
Azure Active Directory B2C provides B2C identity services—customers get single sign-on (SSO) access to applications and APIs using their preferred social, enterprise or local account ID. Another option is to integrate Azure AD with on-premises Active Directory for hybrid and Azure migration scenarios.
You can also use Azure Role-Based Access Control (RBAC) to manage access to cloud resources. RBAC lets you manage who can access your Azure resources, what they can do with them, and which regions can access them.
You can also use the Microsoft Identity Platform to protect DevOps tools themselves, including native support for Azure DevOps and integration with GitHub Enterprise.
Exposed secrets are a severe security issue that is very common in modern applications. Azure Key Vault lets you manage the distribution of secrets by storing them centrally in Azure Key Vault. Key Vault greatly reduces the chance of accidentally disclosing secrets. With Key Vault, application developers no longer need to store credentials or other sensitive information in their application code.
Azure Policy lets you specify a default allowed configuration that is automatically applied to all cloud resources. This can avoid misconfigurations that violate security policies. Azure Policy works on the basis of desired state configuration (also known as declarative configuration), letting you specify to what degree resources and services should be secured and whether to alert or block/modify deployments in Azure if they don’t meet the policy.
When using Azure in multi-tenant mode, policies can be applied at the management group, subscription or resource group level. You can define specific policies and enforce compliance requirements for test, staging, and production environments.
The Azure Security Center (ASC) and Advisor can also be easily integrated into the DevOps process. ASC makes it possible to augment event streams in Azure with threat intelligence or analysis by third-party security tools.
Penetration testing is the recommended method for examining your environment for infrastructure or application configuration vulnerabilities that could create vulnerabilities that an attacker could exploit.
An Azure penetration test should check for vulnerabilities on endpoints, apply fuzzing (malformed input) to discover business logic vulnerabilities, and perform port scanning to identify network vulnerabilities.
Microsoft provides specific guidance for penetration testing in Azure with recommended products and penetration testing service providers.
To ensure security for cloud subscriptions and resource configuration across multiple subscriptions, you can use the tenant security solution part of the Secure DevOps Kit for Azure (AzSK).
In addition, you can leverage Microsoft security technologies like Microsoft Defender for Cloud and Microsoft Sentinel. These solutions provide monitoring and security features designed to detect and alert on unusual events or configurations that require investigation and possible remediation.
In this article, I introduced DevSecOps and showed how the Azure cloud provides a range of services that let you easily implement DevSecOps practices. These include:
I hope this will be useful as you take your first steps to practice DevSecOps in the cloud.
Welcome to The Long View—where we peruse the news of the week and strip it to the essentials. Let’s work out what really matters.
This week: No remedy for banned Facebook users, new ARM-based VMs on Microsoft Azure, and The Great Resignation will still be a Thing for the foreseeable. (more…)
Business transformation and disruption is all about data. But it’s not just about collecting huge amounts of data, it’s what you do with that data and how it can enable you to create powerful insights.
While many people focus on the cloud unicorns, the reality is that industry incumbents have a huge advantage. These businesses have decades of experience which translates into massive amounts of data and business logic. Where do many of these businesses host their most important workloads and data? IBM zSystems—the system that is best known as “the mainframe.”
Today, IBM is announcing its latest IBM zSystem—IBM z16. Two weeks ago, IBM spent time briefing the industry analyst community on the business strategy and technical underpinnings of the IBM z16. We met with a variety of IBM business leaders including executives from the IBM Systems division, Distinguished Engineers and IBM cloud leaders. The most significant updates to the platform are the inclusion of a dedicated on-chip AI accelerator and the addition of quantum-safe cryptography.
It’s important to understand the size of the IBM zSystems ecosystem. The platform is used by 45 of the 50 largest banks in the world, four of the top five airlines and eight of the top 10 insurers, among others. Likewise, the partner ecosystem is huge, ranging from the largest system integrators to small DevOps- and AI-focused technology vendors. Suffice it to say, some of the world’s largest businesses and governments run on IBM zSystems.
In this article, I will briefly cover the five biggest takeaways from the pre-announcement industry analyst briefings.
Many cloud data warehouse and analytics vendors say “Bring me the data.” IBM is flipping the script, because IBM zSystems already houses clients’ most critical customer and operational data. IBM z16 will be shipped with an on-chip integrated accelerator for AI.
Everyone knows that AI creates a competitive advantage, so why should you care about the on-chip AI accelerator? The last mile of deploying machine learning/artificial intelligence (ML/AI) models is still a huge challenge. Businesses are investing large amounts of money and skills in developing models; however, latency often kills. The difficulty is that ML/AI models need to be built into applications so that the model can be executed at the point of transaction with virtually no delay. This also must be done at a massive scale! Think about the latency and security concerns if near-real-time banking transactions needed to go from a back-end system to a cloud AI platform to check for fraudulent transactions.
For those of you who have been following the IBM zSystems platform, IBM’s z14 unveiled a pervasive encryption capability. This encrypt-by-default approach without traditional encryption overhead was enabled through an encryption-dedicated chip. Similarly, AI workloads will be performed by the integrated AI accelerator.
Developers and data scientists want to use data, ML and AI tools that they know and trust. AI success requires an open stack of software options, not a walled garden. While IBM has a wide range of AI technologies, including Watson Machine Learning for z/os, IBM Cloud Pak for Data and IBM Cloud Pak for Watson AIOps, just to name a few, an IBM-only approach would be a non-starter for many data scientists.
The IBM z16 AI accelerator supports a wide selection of ML libraries and compilers. Clients can build and train models in a variety of popular frameworks including TensorFlow, SAS, Microsoft CognitiveToolkit (Microsoft CNTK), PyTorch and Keras, along with others.
The system uses ONNX, an open source tool to enable interoperability of ML/AI models to prepare and deploy the model on the dedicated AI accelerator.
What does this mean? You can bring your existing data models to IBM z16. In addition, your teams can use their existing preferred vendors while also working with IBM zSystem’s integrated accelerator chip.
Cybercrime is a multibillion-dollar business. Although fairly routine exploits like phishing remain the number-one money generator for cybercriminals, these gangs and nation-state-sponsored organizations are getting more sophisticated. For example, cybercriminals are investing in AI and ML just like traditional businesses. In some cases, cybercrime syndicate engineering employees believe they’re working for a legitimate company.
What is quantum-safe cryptography? The research is focused on identifying algorithms that are resistant to attacks by both classical and quantum computers.
Why is this a growing area of concern? IBM, along with Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP) all offer quantum computing-as-a-service. While quantum computing has massive potential for applications across a wide range of industries including drug discovery, chemical engineering and combating climate change, keep in mind that cybercriminals are looking at this technology to unlock complex cryptography algorithms.
IBM claims that IBM z16 is a “quantum-safe system.” The company uses a variety of different hardware, software and business process approaches to assure data security. For example, the system includes a new crypto express card with quantum-safe APIs to upgrade existing application security. At a firmware level, IBM z16 has a built-in dual signature scheme.
In addition, for a number of years, the IBM zSystems platform has taken a zero-trust approach to security which includes a bring-your-own-key (BYOK) framework. IBM can never access the keys to your encrypted data–and neither can cybercriminals and/or governmental organizations—instead, those encryption keys remain inside your firewall.
What a difference a nine can make! Five nines mean 99.999% availability while four nines (99.99% availability) result in more than 47 minutes of downtime per year.
When I work with C-level executives that have a mainframe within their environment, the role of IBM zSystems is always discussed. In many cases, businesses have explored the possibility of transitioning off of the mainframe, but resilience, predictability and security are the reasons why businesses remain on the platform.
IBM mainframe services are increasingly being offered in the cloud. Most recently, IBM announced IBM Z testing in the cloud. Likewise, IBM’s Cloud Hyper Protect Crypto Services, a key management approach using a specific security chip module, is enabled by IBM zSystems technology.
However, you shouldn’t expect mainframe-as-a-service. Between latency, security, resilience, governance and compliance, it’s going to be a long time until you migrate and/or send your mainframe workloads to the cloud.
As the author of Hybrid Cloud for Dummies and Cloud for Dummies 2nd Ed. (among other books and articles) I can confidently say that the cloud is not a panacea. The business case for moving many mainframe workloads to the cloud doesn’t exist.
Don’t get me wrong, mobile check deposits, airline ticketing and contactless hotel check-ins are great, but in many cases, they rely on older systems that have not been updated.
A few weeks ago while traveling for work, my flights were delayed for hours. I saw that another flight was leaving within the hour. I was already at the airport and figured I could rebook it via the airline’s app. Of course, it wasn’t that easy—I finally went to the ticketing desk and told the agent that I’d like to book a different flight. The agent then went to work; they spent a long time entering information into their system, talking to a colleague, asking for help and asking me several questions. After about ten minutes my flight was finally rebooked.
This airline has a great app experience when things are running smoothly (so great, in fact, that I prefer to book my travel on the app rather than the computer). However, even a slight disruption caused everything to go off the rails. It’s clear to me that the company still relies on old, broken processes and outdated technology.
Moving AI closer to the data just makes sense. The last mile of AI—moving models into transactions—continues to be a huge challenge. I work with many businesses that have tons of data and do an excellent job retrospectively analyzing that data. However, real-time data-driven decision-making remains a challenge because of latency and the fact that moving data between on-premises and cloud systems raises security and governance risks (in addition to costs).
Quantum-safe security is a growing concern for highly regulated organizations in the banking, insurance and governmental segments. Over the last several years, financial industry CIOs and CISOs/CSOs at large financial services organizations were focused on establishing a zero-trust approach to security. However, as attacks become more sophisticated, security teams are starting to perform risk assessments on their approach to cryptography. The inclusion of quantum-safe cryptography is important for IBM zSystems customers.
Legit Security today revealed that it discovered a privilege escalation vulnerability in GitHub repositories that has since been remediated.
Liav Caspi, Legit Security CTO, said the company worked with GitHub to remediate the issue prior to disclosure. Legit Security researchers reported they found hundreds of GitHub instances rated with over 1,000 stars each that could be subject to the vulnerability.
Caspi said the company also reached out directly to most of those affected sites, including an instance of GitHub that is used to develop the open source Nginx web server that is employed across hundreds of millions of websites.
Cybercriminals could exploit a vulnerable build script to modify code or built artifacts that would result in software being shipped with code controlled by some other entity. The vulnerability could also enable access to sensitive secrets, such as keys, that are used to access cloud services or simply create a malicious pull request.
Legit Security researchers employed a set of risk assessment tools that are part of a larger software-as-a-service (SaaS) platform that enables them to apply security policies across their entire software development life cycle. Earlier this year, the company made available a free Rapid Risk Assessment tool to provide organizations with more insights into their software supply chains. Security research findings, along with other security best practices, are embedded within the policies that can be enforced via the Legit Security platform.
In the wake of a series of high-profile breaches, there is now more focus on the security of software supply chains than ever. Gartner estimated that 45% of organizations worldwide will have experienced attacks on their software supply chains by 2025, a threefold increase from 2021.
Despite those concerns, however, the rate at which new applications are being deployed and updated doesn’t appear to be slowing. Organizations are too dependent on software to encourage customers to slow down the development and deployment rate of applications. It will, however, take years for organizations to properly train developers to correctly employ security tools within their application development processes, otherwise known as DevSecOps best practices, to reduce the number of vulnerabilities that now routinely manifest themselves in production environments. The only practical alternative is to embed more tools that automatically discover vulnerabilities in code during the application development process.
Regardless of the DevSecOps approach, the ultimate goal remains to build more secure applications faster. That can only be achieved if there are more guardrails in place that prevent developers from inadvertently introducing vulnerabilities into an application. The more frequently applications are updated, the more likely it is that a vulnerability will be introduced. Each time those vulnerabilities are discovered after an application is deployed in a production environment, the more expensive they are to remediate. As the volume of vulnerabilities increases, it’s also only a matter of time before the sheer number of security issues that need to be addressed overwhelms both developers and security operations teams alike.
Application security, of course, has not always received the attention it deserves, but demands for increased application security can no longer be ignored.
As companies grow and expand to become global enterprises, IT infrastructure inevitably becomes more complex. When it comes to cloud computing, that usually means maintaining multiple clouds. There are many reasons for using different cloud service providers (CSPs)—some by choice and others by mandate.
For instance, a large enterprise with operations around the world may choose to use an in-country cloud vendor to be closer to the customer, therefore reducing latency and increasing application performance for operations in that country. Alternately, in some countries, data residency laws require that certain kinds of data reside on cloud providers within the country’s borders, which means companies with local operations can only use a cloud provider in that country. Both circumstances lead companies to maintain multiple clouds to facilitate operations at scale. Additionally, a company may decide to use two or more cloud providers as part of a fallback strategy to automatically reroute network traffic to another provider if a major outage occurs.
Regardless of whether the use of multiple CSPs is a choice or a mandate, enterprise IT leaders—and DevOps teams in particular—need to think strategically about multi-cloud environments and design their software delivery ecosystem from the outset to accommodate a multi-cloud strategy.
An effective multi-cloud DevOps strategy is essential for allowing businesses to easily scale application delivery across many different cloud assets and is therefore vital for enterprises operating globally. To create an effective strategy, consider these best practices.
With many CSPs—each with different requirements and tools—companies too often create siloed software development and delivery teams by vendor (AWS, Azure, GCP, etc.) or functional role (security, compliance, etc.). To avoid this pitfall and maintain control and visibility across all cloud providers, it’s important to implement a centralized software delivery platform, ideally one that’s environment agnostic. This will give your DevOps leaders the intelligence and insight they need to manage the entire software delivery ecosystem across multiple clouds anywhere in the world. At the same time, automated delivery processes can ensure quality checks and other milestones aren’t missed regardless of the cloud destination and that reporting, analytics and metrics from a centralized platform can identify and eliminate bottlenecks across different clouds.
Most cloud vendors have their own tools. However, maintaining a multi-cloud environment using vendor-specific tools can lead to lock-in and which can be costly and time-consuming to remediate. Too often, when companies move to the cloud, they use a set of vendor-specific tools for their software delivery processes. That works well initially, but as the company scales and requires a multi-cloud approach, the teams either find themselves heavily siloed without visibility across the whole ecosystem or there is significant technical debt that needs to be overcome. To avoid this, companies need to plan for the use of cloud-agnostic development and deployment tools from the outset so you can easily shift applications to different cloud providers as necessary to keep pace with changing business needs.
As developers assess and use tools and applications for different clouds, environments and use cases, they can create templates and abstracts to share lessons learned across teams, increase productivity and speed development cycles regardless of the destination CSP. For enterprises with more advanced DevOps practices, self-service capabilities—like a shared module catalog—can further speed development processes.
Once your enterprise has embraced a multi-cloud approach and the DevOps systems and processes are in place to efficiently deploy applications across different CSPs, a multi-cloud environment can be used to your strategic advantage—to increase flexibility, speed software delivery and reduce costs. Here’s how:
When you’re not dealing with the confines of country-specific cloud providers, IT and DevOps leaders can strategically align applications to specific cloud providers by region or country to reduce latency, optimize workloads or take advantage of specific service offerings from a particular CSP.
This concept is a great way for DevOps to leverage a multi-cloud architecture. If an application is run in one cloud and you want to add a feature that uses an application from a different cloud in your IT footprint, you can create a multi-cloud cluster using the same underlying cloud database to support both workloads. In this scenario, a production node resides in cloud A and runs in parallel to the read-only node in cloud B. This helps developers avoid the headaches that come with custom scripting as well as the costs of maintaining a backup and restore scenario.
This strategy is somewhat new, but it’s becoming more and more mainstream as competition increases among cloud providers. Here, you use a multi-cloud cluster to shift your workloads among several cloud providers to optimize your cloud environment and take advantage of negotiated discounts, specialized support, higher availability, lower latency or other unique features offered by a specific CSP.
To keep pace with our rapidly changing world, enterprise-scale organizations must be able to manage software delivery across multiple clouds. If your DevOps systems and processes aren’t designed for flexible development and delivery across different CSPs, you could limit your ability to adjust and successfully manage major disruptions or scale to take advantage of new opportunities.